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@ -1,7 +1,7 @@
# kaldifeat
Feature extraction compatible with kaldi using PyTorch, supporting
CUDA, batch process, and autograd.
Feature extraction compatible with `Kaldi` using PyTorch, supporting
CUDA, batch processing, chunk processing, and autograd.
# Usage
@ -32,14 +32,15 @@ wave = wave.squeeze()
opts = kaldifeat.FbankOptions()
opts.frame_opts.dither = 0
# Yes, it has same options like kaldi
# Yes, it has same options like `Kaldi`
fbank = kaldifeat.Fbank(opts)
features = fbank(wave)
```
To compute features that are compatible with kaldi, you can scale
the wave samples to the range `[-32768, 32768]`.
To compute features that are compatible with `Kaldi`, wave samples have to be
scaled to the range `[-32768, 32768]`. WARNING: You don't have to do this if
you don't care about the compatibility with `Kaldi`
The following is an example:
@ -81,7 +82,7 @@ The output is:
13.94753 19.94101 25.4494 24.90511 17.00044 13.92074 11.66673 11.82172 10.34108 10.72575 10.09829 9.810879 9.676199 9.421767 9.124647 8.774353 9.086291 8.74897 8.469534 8.670973 8.772754 8.740549 8.982433
```
You can see that ``kaldifeat`` produces the same output as kaldi (within some tolerance due to numerical computation).
You can see that ``kaldifeat`` produces the same output as `Kaldi` (within some tolerance due to numerical computation).
**HINT**: Download [test_scp][test_scp] and [test_txt][test_txt].
@ -114,7 +115,7 @@ for more examples.
**HINT**: In the examples, you can find that
- ``kaldifeat`` supports batch processing as well as chunk processing
- ``kaldifeat`` uses the same options as kaldi's `compute-fbank-feats` and `compute-mfcc-feats`
- ``kaldifeat`` uses the same options as `Kaldi`'s `compute-fbank-feats` and `compute-mfcc-feats`
# Installation